Seller categorization

ABSTRACT

Disclosed are methods for competitive pricing implemented in software for commerce in an electronic marketplace. A vendor may identify rivals: competitors against which the vendor most directly competes by dint of having similar reputation and market positioning. Using specified pricing models applied to filtered price data periodically gathered by spider-crawling competitors on the Internet, a vendor may adjust product prices to match rivals while accounting for overall marketplace price trends.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. patent application Ser. No.10/870,855, and thereby claims the benefit of the priority date of Jun.16, 2004.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The relevant technical field is market-based pricing implemented insoftware for commerce in an electronic marketplace, using pricingmodels, and allowing a vendor to identify and better compete againstrivals, a subset of all sellers in a marketplace.

2. Description of the Related Art

Including Information Disclosed Under 37 CFR 1.97 and 1.98

The World Wide Web (the web) portion of the Internet provides apreviously undreamt-of variety of goods and services at competitiveprices as sellers vie in the broadest example of consumer capitalism theworld has experienced.

To remain competitive, sellers must constantly be aware of the prices oftheir competitors. This problem is particularly acute on the web, wherea consumer can readily compare prices and purchase from a seller ofchoice almost instantly, thus making competition ruthless.

The web site Half.com, for example, is an electronic flea market, wheresellers post offering of the same goods at different prices. From aconsumer perspective, the different prices may be accounted for bydifferent quality in used goods, and a seller banking on its reputationto squeeze slices of what economists call ‘rent’ (excess profit).Reputation and quality equal, the higher-priced good stays a listing,while the lower-priced good is chalked up as a sale.

As U.S. Pat. No. 6,076,070 (Stack) states: “Currently, three mainapproaches exist for performing price comparisons (and therefore,indirectly, price adjustments). A vendor can make periodic comparisonswith competitors' prices, can compare competitors' prices when acustomer complains, or can compare prices when a customer makes a priceinquiry.

“Periodic comparisons of the prices of competitors have drawbacks inthat periodic comparisons may not reliably keep up with changes bycompetitors. Also, a great amount of effort is required in comparing allprices of all common products against a fixed set of competitors.”

The “great amount of effort” Stack writes of becomes greater becausethere is no ‘fixed’ set of competitors on the web: the web continues toexpand, with new competitors entering and others exiting (the ones whosegoods stay as a listing, not a sale). Further, a seller competes mostfiercely against its rivals, other similar sellers in reputation andmarket positioning, not equally against all other sellers in amarketplace. To optimize pricing, a seller must be able to identify andtrack who its rivals are.

Just as some bricks-and-mortar vendors on Main Street positionthemselves as upscale, like department stores Nordstroms and Sak's FifthAvenue, renowned for quality products and service to customersatisfaction at a premium price, or for-the-masses, like the cut-ratesales warehouses Wal-Mart and Costco, e-commerce vendors market-positionthemselves, even if that concept may seem less obvious on the web.Obviously, all e-commerce web sites are not created equal: some areeasier (or harder) to: 1) search and browse for products; 2) know aboutproducts from the information given; 3) compare products; 4) buy from;5) reach and deal with customer service (including returns). Sellersthat excel in these factors can afford to price their products at apremium and still rack up sales. Those that excel in the intangibles andprice competitively are world-beaters.

Because of certain factors, such as shipping cost and shoppingconvenience, customers often buy multiple items on the web. Given thatscenario, the convenience and transaction peace-of-mind a reputableseller offers may easily offset paying a slightly higher price.

The web site intangibles described above combined with the purchasingcost model (as a contrast, volume versus boutique (specialty item)purchasing) shape a seller's pricing model. The pattern of pricing aseller uses expresses the pricing model that represents the seller'smarket positioning. Pricing patterns tell the story of the pricingmodel. So, being competitive is appropriately viewed not as just havingthe lowest prices, but as having a successful pricing model againstrivals who qualify as direct competition.

It is facile to think that, on the web, sellers of the same productcompete on price alone. As on Main Street, market positioning results ina stratification of e-commerce sites. While it is relatively easy forcustomers to click from site to site, purchasing has a transaction cost,most tangibly shipping cost, but other costs relating to customerconvenience and pleasure in the shopping experience, including the timeit takes to find and purchase and item, and consumer peace of mind thatthe sellers delivers product in a timely manner with proper orderfulfillment.

Relevant prior art is summarized below for the reader's convenience.

U.S. Pat. No. 6,076,070 (Stack) “Apparatus and method for on-line pricecomparison of competitor's goods and/or services over a computernetwork”: discloses seeking a price comparison upon customer request ofcompetitors' prices for an item. As quoted earlier, Stack explicitlydismisses the utility of periodic competitive price comparison, and sofails to anticipate the value of that mechanism. Stack particularlyfails to anticipate using periodic comparison on a variety of items forsale as a regular method for price-setting. Stack does anticipate usinga vendor-set threshold for determining whether a price adjustment may beoffered to a user or automatically made. Stack only considers loweringprices, and does not consider the prospect of raising prices. Stack doesanticipate that a price reduction from competitive analysis may betemporary or made the regular price.

U.S. Pat. No. 5,960,407 (Vivona) “Automated market price analysissystem”: discloses “a system for estimating price characteristics of aproduct from classified advertisements”. With an exemplary preferredembodiment relating to job offerings, digitized newspapers ads arecategorized based upon keyword lexical analysis of ad content. From thedata, a price curve is developed. Oddly, prices are assumed to have anormal distribution, and deviation analysis performed with thatassumption, regardless of the actual price curve. Regression analysis isperformed to distinguish between different qualities in the data. Thismay make some sense with labor price data, but cannot be construed anappropriate generalized method for accurate derivation of competitiveproduct price data, as it fails to accurately account for discrepancies.Vivona's approach therefore risks polluting the quality of collectedprice curve data to satisfy the statistical desire for a larger samplesize. Vivona does not anticipate filtering data points or accounting fordiscrepancies in formulation of a price curve. From the results of theprice curve and regression analysis, Vivona determines average price.All Vivona's analyses are performed with respect to average price.Vivona does not anticipate different measurements other than average.Vivona does not anticipate competitive price analysis using a subset ofmarketplace prices based upon characterization of seller pricing models.

U.S. Pat. No. 5,873,069 (Reuhl) “System and method for automaticupdating and display of retail prices”: discloses “a computerized pricecontrol system for implementing pricing standards/policies”. “Theprice-changing function of the system is responsive to competitive pricedata on identical or substantially similar products in multiplegeographic markets for multiple competitors.” Reuhl relies upon datagleaned in shopping surveys and input into a computer by hand, notgathered electronically. Reuhl periodically updates prices, asfrequently as twice a day. Reuhl only anticipates changing the price tothe lowest price found in a geographic are: “the price change functionof the system in accordance with the present invention for pricingproducts in the database implements a pricing strategy (logic) whereinthe system user's price is the lowest price in a specified geographicarea on a product-by-product basis”. Reuhl does not consider pricestability, that is, the negative effect on consumer perception ofconstantly fluctuating prices.

U.S. Pat. No. 5,822,736 (Hartman) “Variable margin pricing system”:discloses a method that “generates retail prices based on customer pricesensitivity”. Hartman bases the method on the assumption that“customers' retail price sensitivity increases as the magnitude of thedollar value of the transaction increases and this increasingsensitivity must be offset with a corresponding decrease of gross profitmargin”. Hartman does not postulate a competitive pricing model; thesystem is cost-plus based, with customer price sensitivity being theonly independent variable. Competitors' prices are incorporated onlyindirectly, as reflected in consumer sensitivity: for example, a productoften discounted by competitors is considered as

BRIEF SUMMARY OF THE INVENTION

The disclosed technology facilitates a web vendor to post competitiveprices while maintaining profitability in the ever-changing world of webe-commerce. One aspect of the disclosed technology is identifyingrivals, where rivals are the direct competition to a vendor, othersellers with similar reputation and market positioning to a vendor.Another aspect is pricing competitive against rivals, as contrasted tosetting prices based upon all sellers in a marketplace. A variety ofstatistical methods may be applied to best fit a vendor's salesstrategy, as disclosed herein.

The disclosed technology facilitates fine-tuning profitability byoptimizing product profit-margin mixes via application of variouspricing models. The purpose of the disclosed technology is facilitatingimplementation of a sales strategy using sophisticated econometrictechniques never before so readily realized, but now possible because ofthe computerized nature of e-commerce.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 depicts sellers in a marketplace.

FIG. 2 depicts seller reputation aspects.

FIG. 3 a-3 b depicts crawling other vendors' sites.

FIG. 4 a-4 b depicts exemplary pricing models.

FIG. 5 a-5 d depicts database record structures.

FIG. 6 a-6 c depicts product market-price curves.

FIG. 7 a-7 c depicts the process of competitive market-based pricing.

DETAILED DESCRIPTION OF THE INVENTION

The following U.S. patents are herein incorporated by reference: U.S.Pat. No. 6,687,734 (Sellink) “System and method for determining if oneweb site has the same information as another web site”; U.S. Pat. No.6,631,369 (Meyerzon) “Method and system for incremental web crawling”;U.S. Pat. No. 6,618,717 (Karadimitriou) “Computer method and apparatusfor determining content owner of a website”; U.S. Pat. No. 6,681,255(Cooper) “Regulating rates of requests by a spider engine to web sitesby creating instances of a timing module”.

FIG. 1 depicts nomenclature distinctions used in this disclosure forclarity's sake. A marketplace of sellers 16 compete to sell products 1.The disclosure takes the viewpoint of an e-commerce vendor 12 eyeing itscompetitors 20 on the web. In sophisticated embodiments of the disclosedtechnology, a subset of all the marketplace competition, a vendor'sperceived direct competitors, hereinafter termed rivals 15, representthe greatest threat to a vendor 12 maximizing sales and profitability,and a vendor 12 plays particular attention to its rivals 15 in pricingcompetitively.

Pricing model 29 is given considerable attention herein, but is part ofreputation 23. Referring to FIGS. 1 and 2, reputation 23 is theperception of a seller's market position. A vendor's 12 rivals 15 arecompetitors 20 whose reputation 23 are within a similarity threshold 13.

The disclosed technology relies upon well-known database technology. Avery common database technology is the relational database, based uponthird-normal form data composition which avoids data redundancy. A setof related data in a relational database may be considered logically asif contained in two-dimensional tables in which attributes or fields arearranged as columns, and each data record of the set appears as a row,with no duplicate rows. Since database is relational, related tableswill share at least one column. Tables are created and maintained by arelational database management system (“RDBMS”) program. Objectdatabases are a relatively recent extension of relational databases,facilitating database storage on the now-common paradigm ofobject-oriented programming.

The disclosed technology may be integrated into a vendor's databasemanagement system (DBMS), or be a stand-alone application using either abuilt-in database, or interfacing an external database product. In oneembodiment, the price adjustment technology is integrated into a DBMSthat translates select database data to displayable web page content, atechnology known to those skilled in the art. In one embodiment, thedisclosed technology is used for user-interactive exploration of pricingoptions. These two embodiments may be employed as different aspects ofthe same application. Using automatic price adjustment, a change inprice generates a new web page for display after updating the databaserecord. If the recommended price adjustment exceeds a preset threshold,a vendor may want to examine the data to determine the price adjustment;in other words, explore pricing options.

FIG. 3 a depicts the process of a vendor 12 computer using a spider 14to crawl 64 competitors' 20 web sites 120-122 to gain pricing and otherrelated data. Over time, as depicted in FIG. 3 b, the mix of competitorschanges, as new competitors 111 arrive and others depart 404. Trackingand characterizing the competitive mix through time are essentialaspects of the sophisticated embodiments of the disclosed technology.New competitor 111 records are created and erstwhile competitor 404records retired as the events transpire. Multiple attempts to access acompetitor's web site should be attempted prior to assigning thecompetitor to oblivion, as temporary access problems do occur.

Spider crawls 64 are conducted on a periodic basis. Spider crawls mayvary in their extent, such as limiting themselves to known rivals 15,frequency, and the targeted information. As a vendor 12 adds new product1, for example, crawl 64 for competitive price-related data 40.

Referring to FIG. 5 a, a portion of vendor 12 database data comprises avendor's overall pricing model 19; reputation 13; and item data 11,which is the set of product 1 information. Herein, for clarity, pricingmodel 19 and reputation 13 refer specifically to a vendor 12, so as tocontrast where necessary to competitors 20 and rivals 15, while overall,the designation used for pricing model 29 and reputation 23 correspondsto all sellers 16.

Referring to FIG. 5 b, at least a portion of competitor 20 data that avendor 12 may maintain comprises an identifier 21, assessments ofreputation 23, which includes overall pricing model 29, and location 22.A location 22 is typically the URL of the home page of the vendor.Competitor item price data 24 relates to records of items that thecompetitor sells 40.

A competitor 20 may have multiple locations 22. The multiple locations22 may be treated as one if there is no discernable difference inpricing model 29, or may be treated as different competitors 29 if thepricing models 29 appear different.

An exemplary product 1 record for a vendor 12 is depicted in FIG. 5 c. Aproduct 1, interchangeably called an item, may be a good or service or“productized service” (a combination of good(s) and service(s)). Thereare two forms of item record (for the vendor or competitor): 1) a staticrecord, representing a point in time; 2) a historical record,essentially a log (which includes current data). FIG. 5 c depicts mostclearly the embodiment of a static point in time, but is illustrative ofa historical record as well. If, in the product 1 record, history iskept of cost 5, breakeven 6, price 3, quantity 7, quality 8, pricingmodel 9, threshold 10, the log comprising these fields, which is keptusing date/time, renders a separate date/time 4 field redundant. Historymay be kept for a limited time or limited number of changes Linkingmultiple static records (which use a date/time 4 for each record)equates to keeping a historical record/log. At least some product pricehistory data must be kept to explore pricing trends.

Continuing with FIG. 5 c, an item 1 record comprises a descriptor 2;price 3; date/time 4 (for each entry); cost 5 (history or at least thecurrent cost of inventory in stock); breakeven point 6, which is theminimal acceptable profit margin; quantity 7; quality rating 8 of theitem, if relevant; pricing model 9 that corresponds to the item, ifdifferent items following different pricing models; and a threshold forautomatic price adjustment 10, if used. Some of the item 1 record fieldsdescribed are optional depending on embodiment. Other fields may beexpected to exist in the database record for items 1, such as thelocation(s) an item may appear on the vendor web site, but these are notgermane to the disclosed technology, and so for simplicity indescription are omitted.

Sellers 16 commonly have different pricing models 49 for different items40; a single seller pricing model is unheard of. The concept is mostsimply understood as putting select items “on sale”, that is, at adiscount, often advertised to draw attention and traffic to a site, andquick sale. Even Wal-Mart, with its “everyday low prices”, has saleitems. Often, sellers 16 class items 40 with different pricing models49. Book stores often discount new general-interest books to promotetraffic. Hardware stores are often competitive with mid-to-high priceitems, such as lawnmowers, because consumers will shop around for suchitems, but are profiteering on light bulbs (because people often buythem only when they must have a replacement, hence are not particularlyprice-sensitive) and small price-point items such as small quantities ofscrews or nails (quibbling over pennies to a consumer; to a seller,those pennies add up). In actual software implementation, it isrecommended to have a database table (record structure) for pricingmodels that may be assigned to different categories of items, where, asa suggestion, items has a pricing model classification field, asdepicted in FIGS. 5 c and 5 d. So, the item pricing model 9 depicted inFIG. 6 b is suggested as referential to a class of items to which apricing model applies.

Referring to FIG. 5 d, information gleaned from competitor's items 40are a slightly different set of those for a vendor's 12 items 1:descriptor 42, price 43, date/time 44, quantity 47 and quantity 48 (ifavailable and relevant to the embodiment). The web page location 46 of acompetitor item 40 may be kept for convenience, however crawling may berequired at times if the item 40 is no longer found at its previouslocation 46. Some items 40 are dropped, others added. These changes areexpected to occur, and records accordingly created or deleted asobsolete (bearing in mind whatever need for historic data). Periodic webcrawling 64 is done particularly to find new items 40. A competitoridentifier 21 maps to the competitor 20 record. As with the vendor item1 record, the competitor item 40 record is in the preferred embodiment ahistorical log.

Discrepancy 45 between a competitor's item 40 and a vendor item 1 may berelevant. For example, it is not uncommon for manufacturers to make manymodels of the same basic product, typically with different models havingslight differences. Different sellers often only choose to carry asubset of a manufacturer's product line. The concept of discrepancy 45is that of substitution in price curve modeling: slight differences maybe accounted for to develop a price curve with more points, rather thaninsisting on strict identicalness. For example, Apex model 29-a may bethe same as Apex model 29-b but without the thingamabob that makes for amanufacturer's-suggested-retail-price (MSRP) difference of $2.00 (whichequates to $1.50 street price difference). In developing a price curvefor Apex model 29-a, Apex model 29-b price data may be input with a$1.50 adjustment. In other words, if the price discrepancy is known, itmay be manually input and thus factored in. A sophisticated automatedmethod to handle such discrepancies, that is, to figure the “streetprice” difference between these different models, is to perform surveys(spider crawls, or other data gathering) to create price curves for bothproducts, and use the average price difference for the discrepancyadjustment. This same method of discrepancy handling to incorporate awider base of data for price curve determination may also be used wherethe difference is qualitative: between new and “like new”, for example.Caution is advised to limit incorporating price data with discrepanciesto narrow confines, as doing so with wide discrepancies, such as thedifference between “like new” and “used” (with “very good” and “good” inbetween), pollutes the data with externalities related to consumerquality preferences, which represent a cultural attribute. Price curvedevelopment using variable item incorporation, that is, taking intoaccount such discrepancies 45, may be implemented by specifying as partof the vendor item descriptor 2 a listing of acceptable substitutes andtheir price differences, and following an appropriate method to quantifydiscrepancies 45.

Currency may be a discrepancy 45: a product priced in a differentcurrency, where the currency can be identified by lexical analysis ofthe symbol of the currency, and/or by the address of the site (whichdiffers by country), and/or text identifying the country of the owner ofthe site. A currency conversion table may be used to translate a productprice into the base currency a vendor uses. One must apply caution insuch incorporation, as different market conditions may account for theprice difference; such externalities can make for an apples-and-orangescomparison that pollutes the price data. The currency conversion tablemay be limited to those areas where currency conversion results inreliable price data. If the price data cannot be converted using thetable, it is excluded from further consideration. U.S. Pat. No.6,199,046, though long-winded in its discourse, is suggested to readersparticularly keen on the topic of currency conversion.

New competitor products 40 may be brought to a vendor's 12 attention byfinding competitors' products 40 that have a discrepancy 45, where as aresult a vendor 12 is notified by software, as the discrepancy isunaccounted for in the database record (but the basic model is withinthe record). An event trigger may occur when encountering a competitorproduct that lies within a general descriptor 2, but outside an alloweddiscrepancy 45 (for automatic incorporation into price curve data); theevent trigger causing an email to be automatically generated with thelocation 46 of the found competitor item 40, or an alert display made ona vendor's computer monitor.

Rule-based triggered actions using a database are known to those skilledin the art, and may be employed to maintain data structures and triggerevents relevant to performing software operations disclosed herein.Software construction of conditional rule-making, in particulardata-based rule triggering, is prior art. For more thorough descriptionof rule-based event management within the context of databaseoperations, suggested references include U.S. Pat. Nos. 5,446,885,5,283,856, 5,615,359, 5,564,047, and 6,560,592, among other referencesthat these patents cite (cited art) and which cite these patents(forward reference patents).

FIG. 4 depicts exemplary pricing models for the mix of product 1 offeredfor sale. As described in the background, the reasons for a pricingmodel vary, but a vendor's 1 overall pricing model 19 is itself acumulative statement based upon pricing pattern relative to othervendors of the same products. Individual item pricing models 9 may beemployed, but it is the overall pattern that comprises a vendor'spricing model. For example, a price competitive 32 vendor mayoccasionally post a loss-leader item, particularly as an advertisingploy, while posting less competitive products at prices that affordprofitability.

Referring to FIG. 4 a, with descriptive pricing models, a loss leader 30as an overall pricing model is a formula for philanthropy or bankruptcyor both. A price leader 31 is consistently aggressivelyprice-competitive, often offering the lowest prices. A price competitive32 vendor is exactly that. Pack competitive 32 tends toward the mostcommon prices for products. An average 34 competitor's prices tend tobe, well, average. Middling competitive 35 tends toward the median priceon items, that is, toward the middle. A targeted competitive 36 pricingmodel is selectively competitive, or competitive within a price band 18that is a subset toward the higher price range 17. A profiteer 37 is aprofit-maximizer, often a boutique or specialty vendor of hard-to-finditems. Note that exemplary gradients of pricing models 29 as depicted inFIG. 4 a are conceptual and descriptive, but are statistically oriented;the resultant difference in prices between pack 32, mean 34, andmiddling 35 competitive may be slight (or not, depending on the shape ofthe price curve). On the web, for competitive products with higherconsumer price sensitivity, small price discrepancies may result insubstantial sales volume differences.

In one exemplary embodiment, referring to FIG. 4 b, pricing models arerepresented as percentile bands, perhaps with thinner bands forpercentiles with more price (data) points, so as to better capturepotential differences between mid-priced vendors. In the exemplaryembodiment depicted in FIG. 4 b, price bands are determined by roughlyequivalent number of data points, mitigated by a minimum and maximumpercent differences in price range. Example 1: starting with the lowestprice, include price data points until 15% of the total data points arewithin the band, not exceeding greater than 15% of the price range(highest price−lowest price), with a minimum band of 5% of the pricerange. This tends to result in 7-8 bands. In FIG. 4 b, the first band(a) is cut off at 15% price range, the second (b) and third (c) fillswith 15% of data points. The modal band (d) may be bounded by a minimumprice range (5%). Example 2 (not depicted): an equivalent number of datapoints, perhaps 20%, always resulting in five bands. Using banding, thepricing model decision is setting the price band 18 to be in, and wherein the band to be (mid-point or a specific edge, for example). Forexample, for a relatively aggressive price competitive model usingexample 2 banding (20% of data points), price at the greater of fivepercent above breakeven or the mean price of the second band. Amiddling-competitive seller may use example 1 banding (15% data points,5% min-15% max price range) and select the average of the fourth bandfrom the high end of the price range. In the example depicted in FIG. 5c, the price might be $105 (band e).

Pricing model characterization can be simple: for example, percentdeviation from the single statistical price point, such as mean 53 ormode 54. Similarly, in some embodiments, pricing model decision-makingrules may be simple, not requiring banding analysis. For example, bydefault, price at five percent less than the mode (the most commonprice) 54, but no less than six percent above breakeven 6.

The rule bases for pricing models 9, and the requisite statisticalanalysis used for selection, may be as simple or complex as desired.Almost as important as pricing model method is a proper mixture ofdifferent pricing models 9 for different items 1 to maximize revenuewhile remaining competitive. This is sales strategy, and beyond thescope of the disclosed technology, as many exogenous factors drive thisprocess, including for example, supply cost considerations.

FIG. 7 depicts the processes of item price setting and adjustment basedupon developing market price curves. These processes may be appliedselectively to products, or used with most or all products that a vendor12 sells. These processes may be repeated periodically, preferably atintervals of days to at most every few weeks, at different times fordifferent product lines, and with different periodicity. Price changesmay occur more frequently for some product lines, justifying differentperiods of market price evaluation. In FIG. 2, processes that may not beexpected to change often are illustrated in outlined boxes; revisitingthese determinations every few months to ensure that they remainappropriate is recommended.

Referring to FIG. 7 a, begin by setting pricing models for items 60. Inone embodiment, set the reputation that a vendor has 61. Determinemarket prices 62 for a mix of products. On an item-by-item basis, derivethe statistical characteristics of an item's price curve 63. Anexemplary item price curve 52 is depicted in FIG. 6 a. Price curves 52may take numerous forms, and should not necessarily be expected to be anormal distribution. The first step in deriving a product market-pricecurve 63 is to crawl competitors' sites for prices of the same orsimilar item 64. Collate records of data points of competitors' prices68, possibly filtering which records are acceptable by consideringdiscrepancies 66 and sale items 67 that might skew the price curve andhence price adjustment. In processing item price data 65 insophisticated embodiments, having collated data 68, determinestatistical points of interest 69, such a mode 54, median 55, anddistribution spread in an item price curve 52, that characterize theitem market-price curve 63. In the simplest of embodiments, deriving theaverage price 53 may suffice.

A sale item can be recognized by scanning the web page or productdescription section with the item listing for specific words or phrases,such as “sale” or “discounted” or “special price”. A dictionary of suchterms may be used to compare to the product description text to discernthat the price is a sale price. The question is how to treat such anitem. A vendor does not necessarily want to match every sale pricefound. Sale items sometimes require a combination or other specialpurchase, something that may be hard to discern from automated software,and so the price may make a false representation. A sale may beshort-lived, a promotional loss leader perhaps, or special purchase,dying a quick death if applying to items in stock, of which there may befew. Yet a sale price may also represent a valid price data point, or bea harbinger. This issue becomes more meaningful if filtering competitorsto match to rivals' prices, which winnows data points, making asale-item price-point a statistically more significant outlier. Onesimplistic answer is to incorporate all data. An alternate embodiment isto consider whether the sale price point is from a rival 15. If so, onlyincorporate if the sale price remains for a specified duration; in otherwords, only if the sale is not temporary (perhaps a close-out or specialpurchase); in this case the sale price is not incorporated the firsttime it appears, but perhaps the second time. Price comparison crawls 64may occur more frequency for items 1 on sale, to better monitor pricechanges. If not a rival 15, incorporate the price point. As explainedbelow, using an embodiment of focusing on rivals, non-rival competitorsmay influence prices, though less powerfully than rivals.

Continuing with FIG. 7 a, in one sophisticated embodiment, determinecompetitors' pricing models 72 as a prelude to determining rivals 77(and subsequent steps in embodiments considering rivals).

Begin by setting a market basket 59. The market basket is the productmix used for statistical analysis in determining competitor pricingmodels 72. Competitor pricing models 29 may be determined 72 using allproducts 1 of interest to a vendor 12 (that competitors 20). The marketbasket may be all products a vendor 12 sells that are subject to thedisclosed processes. In one embodiment, a classic statistical samplingtechnique may be employed to shrink the market basket, that is, reducethe number of items required for determining competitors' overallpricing models 72. In a concept used in market-survey economics, as ameasurement simplification, a select subset of products 1 may beconsidered representative of all products. This sampling technique isperhaps best known to measure inflation in the overall economy. In theUnited States, the Consumer Price Index (CPI) is based upon a runningaverage of prices of carefully-chosen mixture of goods. For purposes ofthe disclosed technology, to simplify analysis, a subset of products 1may be selected to represent the entire product lines that a vendor 12sells. Suggested selection methods include best-selling items indifferent categories, or highest-revenue, or highest-profit, or allthree. Such products form the foundation for a vendor's 12 profits, andhence, its continuing existence; these are the products that matter mostto a vendor 12.

A competitor 20 not selling an item 1 in the market basket does notpresent a problem. At worst, one or more lost data points does not alterthe result. Substitution by accounting for product discrepancy 45 mayretrieve some otherwise lost data points. A market basket may beselected that comprises products not all of which the vendor 12 sells,but may be considered reflective of the marketplace.

Continuing with FIG. 7 a, to determine a competitor's overall pricingmodel 73, determine the competitor's item pricing model 74 for each item1 in the market basket, storing the competitor item pricing model 49 forsubsequent use in collating item pricing models 75 to determine acompetitor's most common pricing model. Using the banding techniquedepicted in FIG. 5 c, for example, consider for each item 1 each band 18in which a competitor's price 43 lies by measuring the number of bandsaway from the mode price 54 (another metric, such as mean price 53, maybe used). Sum the deviations and divide by the total number of items 1in the market basket to determine average deviation. This techniqueallows different numbers of bands 18 for different items 1 by relyingupon deviation from a statistical point of constant significance. Asimpler exemplary technique, not using banding, is using percentdeviation from a statistical point such as mean 53 or mode 54 or lowestprice above cost.

A rival 15 is a competitor 20 that has a similar pricing model 29 with asimilar reputation 23. Rivals are a vendor's direct competitors. If arival 15 has the same quality of reputation 23 as a vendor 12, butconsistently lower prices, the vendor's 12 solvency is temporary, ascustomers migrate to the shopping at the rival 15. If a vendor 12 wentout of business, the vendor's rivals 15 would be expected to grab thelion's share of sales from erstwhile customers of that vendor 12.

While the exemplary embodiment bifurcates competitors into two groups,as depicted in FIG. 1, rivals 15 and non-rivals (other competitors),further differentiation is possible in multiple relative groups withregard to similarity to a vendor 12 using multiple similarity thresholds13. In this embodiment, inclusion of different groups may be used fordifferent analyses. Suggested measurement of price trend 58 usingaggressively price-competitive sellers is one example of specializedcompetitor segmentation.

Referring to FIG. 7 b, determine rivals 77. Determine whether acompetitor is a rival 78 by comparing a competitor's overall pricingmodel 29 and other reputation 23 factors to a vendor 12. In oneembodiment considering rivals 15, the rivals 15 may be hand-picked,possibly without statistical determination. In another embodiment,rivals 15 may be selected entirely upon automated statistical analysis,using a similarity threshold 13 to separate rival 15 from merecompetitor 20. Rival determination 78 may be a combination ofstatistical analysis and human verification. In an embodiment notconsidering rivals, the entire marketplace may be used for competitiveprice determination, all competitors 20 essentially being regarded asrivals 15.

Reputation 23 is a statement of a seller's market position.Conceptually, reputation 23 relates to customer perception of seller 16quality, particularly with regard to the services, both tangible andintangible, offered by a business to its customers: 1) ease in findingthe product that meets a customer's need or want (for web sites, thisincludes easy navigation & good search); 2) attentiveness to detail inproduct presentation (giving sufficient information to make the customercomfortable in making a purchasing decision); 3) facilitating productcomparison; 4) easy purchasing (minimal transaction hassle); 5) customerservice—including a) easy to reach & deal with; b) customer-oriented,simple return policy; 6) aesthetics—making a customer feel welcome &comfortable (for a web site, this includes layout and readability). Aseller's 16 pricing model 29 is also a key factor to consumer perceptionof the seller 16, an essential aspect of reputation 23.

Any seller 16 that sells a substitutable product 1 may be considered acompetitor 20. The market price curve for a product 52 is defined by allsellers 16 for which data is available. A rival 15 to a vendor 12 is adetermination by the vendor 12 that the competitor 20, by its marketpositioning, is a direct competitor: from a paranoid viewpoint, a mostprobable cause of lost sales. Sak's Fifth Avenue (or Nordstrom's) andWal-Mart might sell the same toenail clippers, but the two are notrivals 15: regular customers overlap to a very limited degree. In thereputation 23 factors listed above, particularly 2) product presentationand 6) aesthetics, a wide disparity exists. One could reasonable assesssubstantial gaps between Wal-Mart and Sak's in other factors as well.Put pithily, Wal-Mart customers can only wish they were shopping atSak's Fifth Avenue with Wal-Mart prices. Should Sak's Fifth Avenue'ssales department lose sleep that it is losing sales of toenail clippersbecause it price is 60% or even double Wal-Mart's?: doubtful.

Referring to FIG. 2, measurement of factors relevant to a competitor'sreputation 23 may be determined 79 using software read a web site'spages to glean metrics of factors deemed critical to reputation 23.Software can collate different measures of factors to determine relativereputations 23. The evaluation software may be run as well on the vendor12 to provide a standard for comparison to competitors 20. In additionto pricing model 29, example quantifiable factors include: 1) shippingcosts 100; 2) help pages 102; 3) product information 101; 4) returnpolicy 103; 5) being able to track order status 104; and 6) product mix105.

Quantitatively evaluating reputation is inessential to the disclosedtechnology, but recommended. There are different metrics that may beused to express reputation 23. One embodiment collates weighted measuresof factors that may be quantified to derive a single ordinal indicatorin a set range. For example, all factors together may total a maximumreputation of 100, where 100 is identical to the vendor's reputation 13.Note that, referring to FIG. 1, the competitor most dissimilar to thevendor 25 is unlikely to score zero, because in some factors the overallmost dissimilar competitor 25 may be more like the vendor 12 than othercompetitors 20. Continuing the example, if pricing model is 25% of thetotal, a rival may score 25 if its overall pricing model is identical tothe vendor. A score of 0 is the maximum degrees of separation away fromthe vendor. Consider an embodiment where the overall pricing modelmeasurement is from the mean price. Sum and average the percentdeviations from the mean price for the product mix: the vendor's pricingmodel is at −5%, and one competitor's pricing model is at the maximumaway from the mean (and vendor) at +20%, a net difference of 25% fromthe vendor; that sets the scale at 1 point for every 1% deviation fromthe vendor. So, subtract one point (out of a possible 25) from acompetitor's pricing model measurement for every percent difference awayfrom −5% (the vendor's pricing model score). Suggested reading on thetopic of statistical factor analysis of qualitative (but quantifiable)factors is U.S. Pat. No. 6,606,102; similar methods may be used forsoftware evaluating reputation 79.

Shipping costs 100 are often expressed by cost per item or number ofitems. Automated software may be used to determine item shipping cost.Shipping cost is often a very significant factor in purchase decisions,and shipping costs between vendors can vary widely. Considerable weighton shipping cost as a discriminating reputation factor is recommended.Per-item shipping cost may act as a bellwether statistic.

The total number of words devoted to reputation-enhancing matters mayserve a rough metric of vendor attention to reputation 23. The web sitesection devoted to providing helpful consumer information 102 isindicative, and recommended as a significant measure of a vendor'sreputation 23, as it expresses a seller's attention to informingconsumers to enhance comfort in buying from a seller. With help from aspider 14, for example, sum the word-count of help pages 102: startingat the top help page, then drilling down to linked pages, only includingrelevant pages, which may be determined by checking for the inclusionsomewhere on the page of the word “help” and/or the vendor's name, orinclusion of some words in a dictionary that typically appears on suchpages.

Lawyer language most certainly should be excluded from any word countalgorithm, as it hardly qualifies as customer-friendly. Small print isone indication (the large print giveth and the small print taketh away).A dictionary may be constructed of common legal terminology, where athreshold of word frequency of these words serves to exclude the pagefrom consideration as reputation-enhancing. The length of lawyerlanguage may be used as a slight quantified negative to reputation 23.

A very significant reputation 23 factor is providing product information101 to consumers. The average descriptive word count per item 1, coupledwith average number of photos per item, may account for a significantdegree of reputation 23 evaluation.

Return policy duration is commonly 30 days. Automated software may beused to determine return policy duration. Explanation of return policymay also be considered consumer-friendly. Almost all e-commerce vendorshave a return policy; other than duration, only consumer experience istelling, and such information is not easily statistically collated. Asuggestion 9 is to count return policy as a factor only if duration isless than 30 days, which would be a negative. Not having a return policyturns what is normally a small issue into a decisive consideration.

A software spider 14 may be used to determine whether a seller offersorder status tracking, searching and using lexical analysis of wordslike “order status tracking”.

Product mix 105 may be factored by omission from the selected marketbasket; omission resulting in a slightly lower rating. Anotherquantitative method to measure product mix 105 is to sum the number ofdifferent products offered in a select number of product categoriesdeemed important to a vendor; the higher the count, the higher therating. These two methods may be combined for a fuller accounting of acompetitor's product mix 105 rating.

As disclosed with the above examples, factor-weighed evaluation may becoded in software to determine a competitor's reputation 79. Anexemplary weighing of the factors comprising reputation 23: pricingmodel: 25%; product information: 20%; product mix 15%: shipping costs15%; help information 13%; order tracking 7%; return policy 5%. Thisfactor-weighing may vary depending upon to what degree and whichmeasures a vendor 12 values. A similar factor-weighed evaluation may bemade by a human, supplemented by computer-generated data where availablefor different factors. Vendors are typically aware of who their rivalsare; using quantitative techniques at least for validation is prudent.The knowledge of known rivals 15 may be supplemented by findingcompetitors 20 who have a similar pricing model 29 to a vendor 12 andtriggering software or human investigation to evaluate reputation 79 anddetermine whether a competitor is a rival 78.

The final step in determining whether a competitor 20 is a rival iscomparing the competitor reputation to the vendor standard 80. If thecomparison is within a preset threshold 13, that is, within a setdeviation from the vendor 12, the competitor 20 is considered a rival15. Rivalry may also be decided by taking a set percentage ofcompetitors 20 closest to the vendor 12 in reputation 23 (where allcompetitors 20 considered comprise 100%), in which case the similaritythreshold 13 is a proximity percentage.

Continuing with FIG. 7 b, determine competitive prices for items 82 itemby item 83. In an embodiment considering rivals 15, filter rival toother competitor price points 84. As depicted exemplarily in FIG. 6 b,use the rival price points to determine the rival price curve 85.

FIG. 6 b depicts an example of discrepancy between overall item pricecurve 52 and rival price curve 56. As depicted, rivals 15 price the item1 on average slightly higher than the overall marketplace. Thisdiscrepancy is explained by reputation 23 factors: the vendor 12 may bea well-respected business, known for its excellent business practices.Less well regarded competitors 20 must compete more on price 51. Forexample, Amazon may not consistently have the lowest prices on the web,but it offers a tremendous variety of goods with plentiful productinformation, low shipping costs, and is known for accurate and rapidproduct fulfillment.

Continuing with FIG. 7 b, determine price trend 86. This step isoptional but recommended. FIG. 6 c depicts an exemplary change inpricing for an item 1. A historical item price curve 57 compared to thecurrent item price curve 52 reveals a declining price trend 58. A simplederivation of price trend 58 may be had by comparing mean 53, median 55,or mode 54 prices at sequential points in time. The market price curve52 is recommended to discern price trend 58, though, if the vendor 12competes strongly on price, rival price curves 56 may be more telling,as high-end sellers 16 tend to have “sticky” prices, attempting tomaintain margins as long as feasible. In one embodiment, a vendor 12 mayconsider the most price-competitive sellers 20 the best measure of pricetrend 58; price-competitive sellers 20 being within a specifiedthreshold of having the lowest average prices (the low-end pricingmodels) for a product mix 105.

Referring to FIG. 7 c, set prices for new items 1, or adjust prices onexisting items 89, item by item. Price adjustment may be done uniformlyfor a set of items. This is, items may be grouped such that analysis andadjustment occurs for all items in the group. In this case, an item 1refers to a group of items. Price setting may also be done similarly,using a percentage above cost 5 or breakeven 6. For each item, determinethe price 90 (which may equate to a price adjustment for an existingproduct). The essential step is to consider the current price to theitem competitive price 91, however construed. Depending upon embodiment,the item competitive price may reflect the rival price 56 or marketprice curve 52. Competitive pricing is done by applying the item pricingmodel 92, but other factors in some embodiments play a role indetermining price adjustment 90: the price trend and the price changetimeframe.

Consider the price trend 93: the current competitive price may betransitory to a newly developing more stable price point. Consider theprice change timeline 94: price churning, that is, changing prices toofrequently, potentially confuses potential customers, degradesreputation, and may result in lost sales. Price trend and timeline aredifferent aspects of the same issue: maintaining price stability whilereflecting marketplace changes.

A vendor 12 faces a potential problem with significantly lowering prices(ratcheting prices): if price adjustments are not offered recent buyers,they may return a product conformant with the return policy, and buy areplacement at the lower price. There is of course transaction cost forsuch customers to consider. At least, a feeling of dissatisfaction mayresult.

Between churning and ratcheting prices is a smoother approach:anticipate price trends 93 and stabilize prices. While rival competitiveprice is a primary determinant, price trend 58 acts as an influence oncompetitive pricing. A threshold-based rule system may be used todetermine the price adjustment 90, where the rule system is based on anexus of time frame, change in current competitive price per itempricing model, price trend, and sales trend. Changing cost or breakevenpoints should affect the calculus as well.

For example, by default, a price may be expected to last for 20 dayswithout change with exceptions: >5% change in competitive price withsales declining >10%. A price change may be set to competitive pricewith an influence of price trend and sales trend. For a middlingcompetitive model, if sales are declining and pricing trend 58 isfurther price declination, decrement price the same percentage as marketprice trend for the past survey period. If sales are not declining andprice is declining, decrement price by one-half the percentage as marketprice trend. If competitive price is rising, raise price to match unlesssales are rising greater than the half the percentage of the price rise;the intended effect being don't raise prices if customers are flockingto you because the competition is raising prices. If cost declines,match rival price decline to the extent of maintaining at least the samepercentage profit margin, unless sales are declining and the marketplaceprice trend is declining faster, in which case match or exceed themarketplace percentage price drop for the current period as long as theprice remains above breakeven. As a general rule, do not adjust pricebelow breakeven 6 without notification. As these examples suggest,complex factor-interdependent may be constructed to optimize sales andrevenue generation while maintaining profitability.

Once the price adjustment is determined 90, in one embodiment, a changeover a predetermined threshold 95, which may be a relative or absoluteamount, triggers notification 99 by the software, rather than automaticprice adjustment 98.

A specific example: the price for a vendor item 1 has been stable fortwo months. In the past month, rival prices have gone down 3%, 1% themonth previous. The marketplace trend 58 over the past two months hasshown a decline of 7%, with a 2% the month before and a 5% drop thispast month. Sales are 5% less in the past month. Inventory in stock islow, and replacement stock cost is presently 4% less. Rather than matchthe 4% rival price decline, considering the trend 58, consider loweringthe price by at least 6-8%. A threshold percentage of 5% requiresnotification, so notify the vendor of the suggested price adjustment of6-8%.

In the case of automatically adjusting the price 98, the adjusted price,stored in a database record, may be translated to an updated version ofthe web page displaying the product to which the price belongs. U.S.Pat. No. 5,897,622 discloses such technology. In the case of seekingapproval notification 99, if the notification does not automaticallyinclude the data, the approver may use an interactive tool to explorethe acquired competitive data as a basis to make a pricing decision.

Notification 99 may employ one or more methods: through a softwareapplication user interface alert service, such as a sound and/ordisplay, by electronic messaging, or by email. Numerous techniques ofsoftware-triggered notification are known to those skilled in the art.

Price curves for individual items or groups of items such as depicted inFIGS. 6 a-6 c may be shown to a shopper to demonstrate a vendor'scompetitiveness. The price curve 52 may include a point indicating thevendor's price. Other graphical displays of various forms, such as bargraphs, may be used to the same effect. Scatter diagrams with rivals'prices may be used to show comparative prices. If prices are rising, avendor 12 may depict a price trend 58 diagram, as in FIG. 6 c, to showhow it resists raising its price.

A vendor considering carrying a new product 1 may use the disclosedtechnology to find the market price of the item 1. The disclosedtechnology may be used to quantitatively explore the profitability ofcarrying new items.

The following is claimed:
 1. A computer-implemented method forcategorizing a seller relative to a specific vendor comprising: storingan overall pricing model for a vendor, wherein an overall pricing modelcomprises a position within a range of pricing levels for a set ofon-sale products; storing product-specific data for a plurality ofproducts, determining a product mix comprising a plurality of productsfrom said product-specific data; storing said product mix; reading saidproduct mix as a basis for search; whereby finding, via networkconnection, for a plurality of sellers, comprising at least in part afirst seller, prices for at least some said products in said productmix; storing said found prices; statistically characterizing price datafor a plurality of products in said product mix using said found prices;whereby storing said characterizations; determining an overall pricingmodel for said first seller by comparing said first seller productprices to said stored characterizations; categorizing said first sellerby comparing said first seller's overall pricing model to at least onethreshold of similarity to said vendor's overall pricing model; andstoring said first seller categorization.
 2. The method according toclaim 1, further comprising determining at least one rival to saidvendor.
 3. The method according to claim 2, wherein determining saidrival based upon a plurality of factors related to reputation.
 4. Themethod according to claim 3, wherein said reputation is based at leastin part upon at least one quantitative measure.
 5. The method accordingto claim 1, wherein determining said product mix based at least in partupon at least one statistical measure of a product's financialsignificance to said vendor.
 6. The method according to claim 5, whereindetermining said product mix based at least in part upon a product'scategory within a categorization scheme for said product mix.
 7. Acomputer-implemented method for categorizing a seller relative to avendor comprising: storing product-specific data for a first set ofitems, wherein said first set comprises a plurality of items on sale bya predetermined vendor; determining a product mix comprising a pluralityof items from said first set; storing said product mix; reading saidproduct mix data as a basis for current product price search; wherebyfinding, via network connection, for each of a first set of sellers,current prices for a plurality of items in said product mix; whereinsaid first set of sellers does not include said predetermined vendor,wherein distinguishing said vendor from said first set of sellers by, atleast in part, having access to a plurality of current item prices ofsaid vendor without network search; whereby, respective for each seller,the set of items for which current prices were found comprises a resultset for that said seller; storing said respective first set sellerresult set, wherein said first set of sellers comprises at least asecond seller; statistically characterizing respective item price databy determining data at least partly indicative of an item price curvefor a plurality of items in said product mix; storing saidcharacterizations; deriving respective seller item pricing models for aplurality of items in said respective result set, wherein an itempricing model comprises a position within a relative range of pricinglevels for an item based upon said statistical characterization of itemprice data; deriving vendor item pricing models for a plurality of itemsin said product mix; determining an overall pricing model for saidvendor using, at least in part, a plurality of said vendor item pricingmodels, wherein an overall pricing model comprises a position within anordinal range of pricing levels; storing said vendor overall pricingmodel; determining an overall pricing model for said second seller usinga plurality of said second seller item pricing models; storing saidsecond seller overall pricing model; categorizing said second seller byat least in part comparing said second seller's overall pricing model toat least one threshold of similarity to said vendor's overall pricingmodel; and storing said second seller categorization.
 8. Acomputer-implemented method for categorizing a seller relative to avendor comprising: storing an overall pricing model for a predeterminedvendor, wherein an overall pricing model comprises a position within arange of pricing levels for a set of on-sale items; determining aproduct mix comprising a plurality of vendor on-sale items from aplurality of categories, wherein data for said items is stored in atleast one database, and wherein prices for a plurality of items in saidproduct mix are available for said vendor without network search;storing said product mix; finding, via network connection, sellersdistinct from said vendor, such that a seller qualifies as being withina first set of competitors by representing sale of at least one item insaid product mix; whereby identifying a first set of competitors;storing said first set of competitors; for each item in said product mixfor each said competitor in said first set of competitors: (a)attempting to find current price via network connection; (b) storingsaid current price if available; deriving and storing a plurality ofdata points indicative of an item price curve respectively for aplurality of items in said product mix; for a competitor in said firstset of competitors: (a) deriving and storing an item pricing model foreach item for which current competitor price data is available, whereinan item pricing model comprises a position relative to said item pricecurve; (b) deriving an overall pricing model by, at least in part,collating said competitor item pricing models, wherein said competitoroverall pricing model comprises a position within a range of pricinglevels for said product mix; categorizing at least one first competitorby, at least in part, comparing said first competitor's overall pricingmodel to at least one threshold of similarity to the overall pricingmodel for said vendor; and storing said first competitor categorization.9. The method according to claim 7, wherein deriving said product mixusing, at least in part, comparative statistical analysis of candidateitems for said product mix.
 10. The method according to claim 7, furthercomprising: storing a plurality of predetermined overall pricing models.11. The method according to claim 7, further comprising: determiningsaid product mix based, at least in part, by selection of products in aplurality of predetermined categories.
 12. The method according to claim7, wherein said product mix comprises items selected as representativeof said first set.
 13. The method according to claim 7, furthercomprising: not adding at least one item to said second seller's resultset owing to a discrepancy between the second seller item data and thevendor item data.
 14. The method according to claim 8, wherein at leastone said item price curve is at least partly characterized by aplurality of statistically derived points.
 15. The method according toclaim 8, wherein at least one said item price curve is at least partlycharacterized by an algebraic function.
 16. The method according toclaim 8, wherein qualifying a seller as being within a first set ofcompetitors by representing sale of a plurality of items in said productmix.
 17. The method according to claim 8, further comprising:determining a plurality of item pricing model bands based uponcharacteristics of said item price curve.
 18. The method according toclaim 8, further comprising: determining at least two differentcompetitors in two different categories based, at least in part, uponrespective comparison of competitor overall pricing model to saidvendor.
 19. The method according to claim 8, further comprising:categorizing said first competitor in part, by computer user input intoa database storing data related to said first competitor, wherein saidinput comprises data related to market research, said research exogenousto data relating to said derived item pricing models for said firstcompetitor.